We model a decision impact pathway is for school gardens as a general intervention for sustainable children’s food environments in urban Hanoi, Vietnam (Whitney et al. 2024).
Conceptual model of school gardens as an intervention. Should urban Hanoi school boards invest time and money in creating school gardens? Should they invest in formal STEM education as part of these gardens?
Simulation of the school garden intervention options:
# Source our model
source("Garden_Model.R")
# Ensure consistent results with the random number generator
# not for each 'run' of the MC simulation but for
# consistency each time we run the entire simulation
set.seed(42)
garden_simulation_results <- mcSimulation(
estimate = estimate_read_csv("data/inputs_school_garden.csv"),
model_function = school_garden_function,
numberOfModelRuns = 1e4, #run 10,000 times
functionSyntax = "plainNames"
)
The Net Present Value (i.e. current value of the future benefits) of the garden decision options over 5 years of the intervention. For public and private schools the STEM costs are considered to be in the same garden space but with the additional costs and benefits of a full STEM education program. All options are compared to the same years of using the land for something that is not related to the garden, i.e. as a playground or for parking. Here we plot the distribution for the decision and frame the projected NPV.
For public schools:
source("functions/plot_distributions.R")
plot_distributions(mcSimulation_object = garden_simulation_results,
vars = c("NPV_garden_public_school",
"NPV_garden_STEM_public_school"),
old_names = c("NPV_garden_public_school", "NPV_garden_STEM_public_school"),
new_names = c("NPV public school garden", "NPV public school garden with STEM"),
method = 'smooth_simple_overlay',
base_size = 7,
x_axis_name = "Comparative NPV outcomes")
For private schools:
source("functions/plot_distributions.R")
plot_distributions(mcSimulation_object = garden_simulation_results,
vars = c("NPV_garden","NPV_garden_STEM"),
old_names = c("NPV_garden","NPV_garden_STEM"),
new_names = c("NPV private school garden","NPV private school with STEM"),
method = 'smooth_simple_overlay',
base_size = 7,
x_axis_name = "Comparative NPV outcomes")
The same results again but this time as boxplots:
source("functions/plot_distributions.R")
plot_distributions(mcSimulation_object = garden_simulation_results,
vars = c("NPV_garden","NPV_garden_STEM", "NPV_garden_public_school", "NPV_garden_STEM_public_school"),
old_names = c("NPV_garden","NPV_garden_STEM", "NPV_garden_public_school", "NPV_garden_STEM_public_school"),
new_names = c("NPV private school garden","NPV private school with STEM", "NPV public school garden", "NPV public school garden with STEM"),
method = "boxplot",
base_size = 7,
x_axis_name = "Comparative NPV outcomes")
ggsave("figures/boxplots_all.png", width = 15, height = 8, units = "cm")
As boxplots and distributions for public schools:
source("functions/plot_distributions.R")
plot_distributions(mcSimulation_object = garden_simulation_results,
vars = c("NPV_garden_public_school", "NPV_garden_STEM_public_school"),
old_names = c("NPV_garden_public_school", "NPV_garden_STEM_public_school"),
new_names = c("NPV public school garden", "NPV public school garden with STEM"),
method = "boxplot_density",
base_size = 7,
x_axis_name = "Comparative NPV outcomes")
As boxplots and distributions for private schools:
source("functions/plot_distributions.R")
plot_distributions(mcSimulation_object = garden_simulation_results,
vars = c("NPV_garden","NPV_garden_STEM"),
old_names = c("NPV_garden","NPV_garden_STEM"),
new_names = c("NPV private school garden","NPV private school with STEM"),
method = "boxplot_density",
base_size = 7,
x_axis_name = "Comparative NPV outcomes")
Summary of the NPVs for the passive education garden and STEM options for private schools:
summary(garden_simulation_results$y[1:2]) #"NPV_garden" "NPV_garden_STEM"
## NPV_garden NPV_garden_STEM
## Min. :-1420.0 Min. :-4017.3
## 1st Qu.: -171.5 1st Qu.: -488.5
## Median : 382.3 Median : 128.9
## Mean : 692.8 Mean : 346.2
## 3rd Qu.: 1247.1 3rd Qu.: 982.8
## Max. :10218.6 Max. :10175.3
Summary of the NPVs for the passive education garden and STEM options for public schools:
summary(garden_simulation_results$y[3:4]) #"NPV_garden_public_school" "NPV_garden_STEM_public_school"
## NPV_garden_public_school NPV_garden_STEM_public_school
## Min. :-1420.0 Min. :-4017.34
## 1st Qu.: -285.7 1st Qu.: -460.28
## Median : -184.5 Median : -241.74
## Mean : 288.3 Mean : -56.18
## 3rd Qu.: 590.5 3rd Qu.: 121.57
## Max. : 7138.2 Max. : 6583.17
Summary of the child health outcomes for private and public schools:
summary(garden_simulation_results$y[6:7]) #"health" "health_STEM"
## health health_STEM
## Min. : 0.0 Min. : 0.0
## 1st Qu.: 304.8 1st Qu.: 278.1
## Median : 768.9 Median : 635.2
## Mean : 836.7 Mean : 651.8
## 3rd Qu.:1227.3 3rd Qu.: 957.4
## Max. :5452.9 Max. :3775.8
Summary of the biodiversity outcomes for the passive education garden and STEM options for private and public schools:
summary(garden_simulation_results$y[5]) #"biodiversity"
## biodiversity
## Min. : 0.000
## 1st Qu.: 4.278
## Median :11.194
## Mean :11.300
## 3rd Qu.:16.864
## Max. :64.952
Total expected costs for a school garden with and without STEM education:
summary(garden_simulation_results$y[8:9])
## total_costs total_costs_STEM
## Min. : 87.33 Min. : 143.1
## 1st Qu.: 199.98 1st Qu.: 357.2
## Median : 435.41 Median : 839.6
## Mean : 398.83 Mean : 929.8
## 3rd Qu.: 514.87 3rd Qu.:1252.3
## Max. :1474.13 Max. :5011.9
First year expected costs for a school garden:
summary(garden_simulation_results$y$Cashflow_garden1)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -606.721 -95.383 8.306 65.859 174.797 1869.821
First year expected costs for a school garden with STEM education:
summary(garden_simulation_results$y$Cashflow_garden_STEM1)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -943.72 -237.06 -121.76 -77.60 44.09 1772.05
We use Projection to Latent Structures (PLS) model to assess the
correlation strength and direction for model variables and outcome
variables. The Partial Least Squares is fitted with the orthogonal
scores algorithm with pls::plsr.
PLS for private schools:
# For passive education garden option
source("functions/pls_model.R")
pls_result <- pls_model(object = garden_simulation_results,
resultName = names(garden_simulation_results$y)[1], # the "NPV_garden"
ncomp = 1)
# read in the common input table
input_table <- read.csv("data/inputs_school_garden.csv")
label_private_school <- "Private school"
# source the plot function
source("functions/plot_pls.R")
plot_pls_garden <- plot_pls(plsrResults = pls_result,
input_table = input_table,
threshold = 0.9) +
theme(legend.position = "none", axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks = element_blank()) + scale_x_continuous(limits = c(0, 7)) + ggtitle(label_private_school) +
annotate(geom="text", x=5, y=1, label="Garden")
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
#For school garden with formal STEM education
pls_result_STEM <- pls_model(object = garden_simulation_results,
resultName = names(garden_simulation_results$y)[2], # the "NPV_garden_STEM"
ncomp = 1)
plot_pls_STEM <- plot_pls(plsrResults = pls_result_STEM,
input_table = input_table,
threshold = 0.9) +
scale_x_continuous(limits = c(0, 7)) +
annotate(geom="text", x=5, y=1, label="STEM garden")
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
plot_pls_garden / plot_pls_STEM
Garden options for private schools:
source("functions/pls_posthoc.R")
pls_posthoc(plsrResults = pls_result, threshold = 0.9)
## Data: X dimension: 10000 75
## Y dimension: 10000 1
## Fit method: oscorespls
## Number of components considered: 1
## TRAINING: % variance explained
## 1 comps
## X 1.378
## y 79.536
## Data: X dimension: 10000 75
## Y dimension: 10000 1
## Fit method: oscorespls
## Number of components considered: 1
## TRAINING: % variance explained
## 1 comps
## X 1.378
## y 79.536
## Data: X dimension: 10000 75
## Y dimension: 10000 1
## Fit method: oscorespls
## Number of components considered: 1
## TRAINING: % variance explained
## 1 comps
## X 1.378
## y 79.536
## PLS Model Summary:
## Number of Components: 1
## R-squared Value for Y:
## % Variance Explained in X:
## % Variance Explained in Y:
##
## Important Variables (VIP > 0.9):
## Variable VIP Coefficient
## if_community_likes if_community_likes 3.966649 476.7332
## school_event_value school_event_value 6.784288 815.3724
## school_event_freq school_event_freq 3.350710 402.7064
## $plsrResults
## Partial least squares regression, fitted with the orthogonal scores algorithm.
## Call:
## plsr(formula = y ~ x, ncomp = ncomp, method = "oscorespls", scale = scale)
##
## $r_squared
## NULL
##
## $explained_variance_x
## NULL
##
## $explained_variance_y
## NULL
##
## $important_vars
## Variable VIP Coefficient
## if_community_likes if_community_likes 3.966649 476.7332
## school_event_value school_event_value 6.784288 815.3724
## school_event_freq school_event_freq 3.350710 402.7064
STEM options for private schools:
pls_posthoc(plsrResults = pls_result_STEM, threshold = 0.9)
## Data: X dimension: 10000 75
## Y dimension: 10000 1
## Fit method: oscorespls
## Number of components considered: 1
## TRAINING: % variance explained
## 1 comps
## X 1.381
## y 74.287
## Data: X dimension: 10000 75
## Y dimension: 10000 1
## Fit method: oscorespls
## Number of components considered: 1
## TRAINING: % variance explained
## 1 comps
## X 1.381
## y 74.287
## Data: X dimension: 10000 75
## Y dimension: 10000 1
## Fit method: oscorespls
## Number of components considered: 1
## TRAINING: % variance explained
## 1 comps
## X 1.381
## y 74.287
## PLS Model Summary:
## Number of Components: 1
## R-squared Value for Y:
## % Variance Explained in X:
## % Variance Explained in Y:
##
## Important Variables (VIP > 0.9):
## Variable VIP Coefficient
## if_community_likes if_community_likes 3.789712 484.8643
## annual_teacher_training annual_teacher_training 2.809487 -359.4521
## school_event_value school_event_value 6.350470 812.4936
## school_event_freq school_event_freq 3.123342 399.6074
## $plsrResults
## Partial least squares regression, fitted with the orthogonal scores algorithm.
## Call:
## plsr(formula = y ~ x, ncomp = ncomp, method = "oscorespls", scale = scale)
##
## $r_squared
## NULL
##
## $explained_variance_x
## NULL
##
## $explained_variance_y
## NULL
##
## $important_vars
## Variable VIP Coefficient
## if_community_likes if_community_likes 3.789712 484.8643
## annual_teacher_training annual_teacher_training 2.809487 -359.4521
## school_event_value school_event_value 6.350470 812.4936
## school_event_freq school_event_freq 3.123342 399.6074
# For passive education garden option
source("functions/pls_model.R")
pls_result_garden_public <- pls_model(object = garden_simulation_results,
resultName = names(garden_simulation_results$y)[3],
# "NPV_garden_public_school"
ncomp = 1)
# read in the common input table
input_table <- read.csv("data/inputs_school_garden.csv")
label_public_school <- "Public school"
# source the plot function
source("functions/plot_pls.R")
plot_pls_garden_public <- plot_pls(pls_result_garden_public,
input_table = input_table, threshold = 0.9) +
theme(legend.position = "none", axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks = element_blank()) +
scale_x_continuous(limits = c(0, 7)) + ggtitle(label_public_school) +
annotate(geom="text", x=5, y=1, label="Garden")
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
#For school garden with formal STEM education
pls_result_STEM_public <- pls_model(object = garden_simulation_results,
resultName = names(garden_simulation_results$y)[4],
# "NPV_garden_STEM_public_school"
ncomp = 1)
plot_pls_public_STEM <- plot_pls(pls_result_STEM_public,
input_table = input_table, threshold = 0.9) + scale_x_continuous(limits = c(0, 7)) +
annotate(geom="text", x=5, y=1, label="STEM garden")
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
plot_pls_garden_public / plot_pls_public_STEM
Garden option in public school:
source("functions/pls_posthoc.R")
pls_posthoc(plsrResults = pls_result_garden_public, threshold = 0.9)
## Data: X dimension: 10000 75
## Y dimension: 10000 1
## Fit method: oscorespls
## Number of components considered: 1
## TRAINING: % variance explained
## 1 comps
## X 1.379
## y 45.493
## Data: X dimension: 10000 75
## Y dimension: 10000 1
## Fit method: oscorespls
## Number of components considered: 1
## TRAINING: % variance explained
## 1 comps
## X 1.379
## y 45.493
## Data: X dimension: 10000 75
## Y dimension: 10000 1
## Fit method: oscorespls
## Number of components considered: 1
## TRAINING: % variance explained
## 1 comps
## X 1.379
## y 45.493
## PLS Model Summary:
## Number of Components: 1
## R-squared Value for Y:
## % Variance Explained in X:
## % Variance Explained in Y:
##
## Important Variables (VIP > 0.9):
## Variable VIP
## if_community_likes if_community_likes 3.7987712
## school_event_value school_event_value 6.6610439
## school_event_freq school_event_freq 3.2684011
## suitability_of_land_for_garden suitability_of_land_for_garden 0.9145923
## beurocratic_barriers beurocratic_barriers 1.3202388
## Coefficient
## if_community_likes 297.94033
## school_event_value 522.43043
## school_event_freq 256.34303
## suitability_of_land_for_garden 71.73213
## beurocratic_barriers -103.54727
## $plsrResults
## Partial least squares regression, fitted with the orthogonal scores algorithm.
## Call:
## plsr(formula = y ~ x, ncomp = ncomp, method = "oscorespls", scale = scale)
##
## $r_squared
## NULL
##
## $explained_variance_x
## NULL
##
## $explained_variance_y
## NULL
##
## $important_vars
## Variable VIP
## if_community_likes if_community_likes 3.7987712
## school_event_value school_event_value 6.6610439
## school_event_freq school_event_freq 3.2684011
## suitability_of_land_for_garden suitability_of_land_for_garden 0.9145923
## beurocratic_barriers beurocratic_barriers 1.3202388
## Coefficient
## if_community_likes 297.94033
## school_event_value 522.43043
## school_event_freq 256.34303
## suitability_of_land_for_garden 71.73213
## beurocratic_barriers -103.54727
STEM option in public school:
pls_posthoc(plsrResults = pls_result_STEM_public, threshold = 0.9)
## Data: X dimension: 10000 75
## Y dimension: 10000 1
## Fit method: oscorespls
## Number of components considered: 1
## TRAINING: % variance explained
## 1 comps
## X 1.378
## y 52.799
## Data: X dimension: 10000 75
## Y dimension: 10000 1
## Fit method: oscorespls
## Number of components considered: 1
## TRAINING: % variance explained
## 1 comps
## X 1.378
## y 52.799
## Data: X dimension: 10000 75
## Y dimension: 10000 1
## Fit method: oscorespls
## Number of components considered: 1
## TRAINING: % variance explained
## 1 comps
## X 1.378
## y 52.799
## PLS Model Summary:
## Number of Components: 1
## R-squared Value for Y:
## % Variance Explained in X:
## % Variance Explained in Y:
##
## Important Variables (VIP > 0.9):
## Variable VIP Coefficient
## if_community_likes if_community_likes 3.596175 306.5634
## annual_teacher_training annual_teacher_training 3.599088 -306.8117
## school_event_value school_event_value 6.115816 521.3553
## school_event_freq school_event_freq 2.975338 253.6388
## $plsrResults
## Partial least squares regression, fitted with the orthogonal scores algorithm.
## Call:
## plsr(formula = y ~ x, ncomp = ncomp, method = "oscorespls", scale = scale)
##
## $r_squared
## NULL
##
## $explained_variance_x
## NULL
##
## $explained_variance_y
## NULL
##
## $important_vars
## Variable VIP Coefficient
## if_community_likes if_community_likes 3.596175 306.5634
## annual_teacher_training annual_teacher_training 3.599088 -306.8117
## school_event_value school_event_value 6.115816 521.3553
## school_event_freq school_event_freq 2.975338 253.6388
Here we assess value of information with the multi_EVPI
function. We calculate value of information in the form of Expected
Value of Perfect Information (EVPI).
# Subset the outputs from the mcSimulation function (y) by selecting the correct variables be sure to run the multi_EVPI only on the variables that we want. Find them with names(garden_simulation_results$y)
mcSimulation_table <- data.frame(garden_simulation_results$x,
garden_simulation_results$y[1:9])
Calculate EVPI:
source("functions/multi_EVPI.R")
# first_out_var is the first result variable in the table, "NPV_garden" in our case.
# names(garden_simulation_results$y)
# evpi <- multi_EVPI(mc = mcSimulation_table, first_out_var = "NPV_garden")
# save as a local .csv (takes ~ a day to run this)
# save(evpi,file="data/data_evpi.Rda")
load("data/data_evpi.Rda")
# open from saved file (last model run)
EVPI for private schools:
#Value of information the garden intervention decision
source("functions/plot_evpi.R")
plot_evpi_garden <- plot_evpi(EVPIresults = evpi,
decision_vars = "NPV_garden",
new_names = "Garden",
input_table = input_table,
threshold = 10) +
theme(legend.position = "none", axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks = element_blank()) +
scale_x_continuous(limits = c(0, 210)) + ggtitle(label_private_school)
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
# Value of information for the garden option with formal STEM education.
# using the results of the same multi_EVPI
plot_evpi_STEM <- plot_evpi(EVPIresults = evpi,
decision_vars = "NPV_garden_STEM",
new_names = "STEM garden",
input_table = input_table,
threshold = 10)+ scale_x_continuous(limits = c(0, 210))
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
plot_evpi_garden / plot_evpi_STEM
EVPI for public schools:
# Value of information for the public school garden option with no formal STEM education.
# using the results of the same multi_EVPI
plot_evpi_public <- plot_evpi(evpi, decision_vars = "NPV_garden_public_school",
new_names = "Garden",
input_table = input_table,
threshold = 10) +
theme(legend.position = "none", axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks = element_blank()) +
scale_x_continuous(limits = c(0, 210)) + ggtitle(label_public_school) #210
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
# Value of information for the public school garden option with formal STEM education.
# using the results of the same multi_EVPI
plot_evpi_public_STEM <- plot_evpi(evpi, decision_vars = "NPV_garden_STEM_public_school",
new_names = "STEM garden",
input_table = input_table,
threshold = 10) +
scale_x_continuous(limits = c(0, 210)) #210
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
plot_evpi_public / plot_evpi_public_STEM
Cash flow plots of the garden option without formal STEM education. These are the expected costs for public and private schools over the intervention.
# Cashflow of the garden option without formal STEM education
# This will be the cost for public and private schools over the intervention.
source("functions/plot_cashflow.R")
plot_cashflow_garden <- plot_cashflow(mcSimulation_object = garden_simulation_results,
cashflow_var_name = "Cashflow_garden",
facet_labels = "Garden") +
theme(legend.position = "none", axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks = element_blank())
# Cashflow of the garden option with formal STEM education
source("functions/plot_cashflow.R")
plot_cashflow_STEM <- plot_cashflow(mcSimulation_object = garden_simulation_results,
cashflow_var_name = "Cashflow_garden_STEM",
facet_labels = "STEM Garden")
plot_cashflow_garden / plot_cashflow_STEM
ggsave("figures/Fig_9_cashflow.png", width=5, height=5)
These figures display the Pareto-optimal solutions, representing the best trade-offs among the objectives of biodiversity, child health, and economic return. By focusing on these Pareto-optimal points, the analysis highlights solutions where improvements in one objective cannot be achieved without some compromise in at least one other.
Private schools Pareto-optimal solutions:
source("functions/plot_pareto.R")
private_pareto <- plot_pareto(
economic_return_garden = garden_simulation_results$y$NPV_garden,
health_garden = garden_simulation_results$y$health,
biodiversity_garden = garden_simulation_results$y$biodiversity,
economic_return_STEM = garden_simulation_results$y$NPV_garden_STEM,
health_STEM = garden_simulation_results$y$health_STEM,
biodiversity_STEM = garden_simulation_results$y$biodiversity,
plot_return = "scatter"
)
ggplotly(private_pareto)
knitr::include_graphics("figures/private_pareto_scatter.png")
knitr::include_graphics("figures/private_pareto_surface.png")
Private school Pareto-optimal solutions interpretation:
source("functions/pareto_posthoc.R")
private_pareto_posthoc <- pareto_posthoc(
economic_return_garden = garden_simulation_results$y$NPV_garden,
health_garden = garden_simulation_results$y$health,
biodiversity_garden = garden_simulation_results$y$biodiversity,
economic_return_STEM = garden_simulation_results$y$NPV_garden_STEM,
health_STEM = garden_simulation_results$y$health_STEM,
biodiversity_STEM = garden_simulation_results$y$biodiversity
)
## Number of Pareto-optimal points for STEM option: 47
## Number of Pareto-optimal points for Garden option: 66
##
## Summary of Pareto-optimal points for STEM option:
## economic_return biodiversity health
## Min. : -576.6 Min. : 718.5 Min. : 7.671
## 1st Qu.: 884.9 1st Qu.:1437.3 1st Qu.:24.519
## Median : 2178.2 Median :1867.4 Median :31.754
## Mean : 2883.6 Mean :2212.3 Mean :31.578
## 3rd Qu.: 4239.7 3rd Qu.:3000.3 3rd Qu.:38.132
## Max. :10218.6 Max. :5452.9 Max. :64.952
##
## Summary of Pareto-optimal points for Garden option:
## economic_return biodiversity health
## Min. :-2564.8 Min. : 402.6 Min. : 7.671
## 1st Qu.: 983.1 1st Qu.:1112.0 1st Qu.:18.787
## Median : 2264.2 Median :1533.7 Median :26.716
## Mean : 2536.1 Mean :1623.1 Mean :28.389
## 3rd Qu.: 4089.9 3rd Qu.:2086.7 3rd Qu.:36.457
## Max. :10175.3 Max. :3775.8 Max. :64.952
private_pareto_posthoc
## $num_pareto_stem
## [1] 47
##
## $num_pareto_garden
## [1] 66
##
## $stem_summary
## economic_return biodiversity health
## Min. : -576.6 Min. : 718.5 Min. : 7.671
## 1st Qu.: 884.9 1st Qu.:1437.3 1st Qu.:24.519
## Median : 2178.2 Median :1867.4 Median :31.754
## Mean : 2883.6 Mean :2212.3 Mean :31.578
## 3rd Qu.: 4239.7 3rd Qu.:3000.3 3rd Qu.:38.132
## Max. :10218.6 Max. :5452.9 Max. :64.952
##
## $garden_summary
## economic_return biodiversity health
## Min. :-2564.8 Min. : 402.6 Min. : 7.671
## 1st Qu.: 983.1 1st Qu.:1112.0 1st Qu.:18.787
## Median : 2264.2 Median :1533.7 Median :26.716
## Mean : 2536.1 Mean :1623.1 Mean :28.389
## 3rd Qu.: 4089.9 3rd Qu.:2086.7 3rd Qu.:36.457
## Max. :10175.3 Max. :3775.8 Max. :64.952
Public schools Pareto-optimal solutions:
source("functions/plot_pareto.R")
public_pareto <- plot_pareto(
economic_return_garden = garden_simulation_results$y$NPV_garden_public_school,
health_garden = garden_simulation_results$y$health,
biodiversity_garden = garden_simulation_results$y$biodiversity,
economic_return_STEM = garden_simulation_results$y$NPV_garden_STEM_public_school,
health_STEM = garden_simulation_results$y$health_STEM,
biodiversity_STEM = garden_simulation_results$y$biodiversity,
plot_return = "scatter"
)
ggplotly(public_pareto)
knitr::include_graphics("figures/public_pareto_scatter.png")
knitr::include_graphics("figures/public_pareto_surface.png")
Public school Pareto-optimal solutions interpretation:
source("functions/pareto_posthoc.R")
public_pareto_posthoc <- pareto_posthoc(
economic_return_garden = garden_simulation_results$y$NPV_garden_public_school,
health_garden = garden_simulation_results$y$health,
biodiversity_garden = garden_simulation_results$y$biodiversity,
economic_return_STEM = garden_simulation_results$y$NPV_garden_STEM_public_school,
health_STEM = garden_simulation_results$y$health_STEM,
biodiversity_STEM = garden_simulation_results$y$biodiversity
)
## Number of Pareto-optimal points for STEM option: 56
## Number of Pareto-optimal points for Garden option: 71
##
## Summary of Pareto-optimal points for STEM option:
## economic_return biodiversity health
## Min. :-576.6 Min. : 0 Min. : 0.00
## 1st Qu.:1222.1 1st Qu.:1092 1st Qu.:21.40
## Median :2839.3 Median :1469 Median :27.83
## Mean :2978.7 Mean :1823 Mean :28.17
## 3rd Qu.:5080.4 3rd Qu.:2199 3rd Qu.:35.67
## Max. :7138.2 Max. :5453 Max. :64.95
##
## Summary of Pareto-optimal points for Garden option:
## economic_return biodiversity health
## Min. :-2564.85 Min. : 151.4 Min. : 7.671
## 1st Qu.: 66.99 1st Qu.:1003.4 1st Qu.:19.026
## Median : 2078.60 Median :1404.3 Median :24.732
## Mean : 2151.91 Mean :1576.8 Mean :27.016
## 3rd Qu.: 3952.36 3rd Qu.:2064.8 3rd Qu.:35.411
## Max. : 6583.17 Max. :3775.8 Max. :64.952
public_pareto_posthoc
## $num_pareto_stem
## [1] 56
##
## $num_pareto_garden
## [1] 71
##
## $stem_summary
## economic_return biodiversity health
## Min. :-576.6 Min. : 0 Min. : 0.00
## 1st Qu.:1222.1 1st Qu.:1092 1st Qu.:21.40
## Median :2839.3 Median :1469 Median :27.83
## Mean :2978.7 Mean :1823 Mean :28.17
## 3rd Qu.:5080.4 3rd Qu.:2199 3rd Qu.:35.67
## Max. :7138.2 Max. :5453 Max. :64.95
##
## $garden_summary
## economic_return biodiversity health
## Min. :-2564.85 Min. : 151.4 Min. : 7.671
## 1st Qu.: 66.99 1st Qu.:1003.4 1st Qu.:19.026
## Median : 2078.60 Median :1404.3 Median :24.732
## Mean : 2151.91 Mean :1576.8 Mean :27.016
## 3rd Qu.: 3952.36 3rd Qu.:2064.8 3rd Qu.:35.411
## Max. : 6583.17 Max. :3775.8 Max. :64.952
Here we provide a summary of the garden intervention options. We do
this with a summary table of the simulation results. We show the
percentage of missing values as well as the mean, median and standard
deviation (SD) for each output of our model simulations. We use the
gt_plt_summary() from {gtExtras} and with options from
{svglite}. The table shows the name, the plot overview as well as the
number of missing values, the mean, median and the standard deviation of
the distribution for all variables that were fed into the model from our
input table of uncertainty values.
# Subset the outputs from the mcSimulation function (y) to summarize only on the variables that we want.
# names(garden_simulation_results$x)
mcSimulation_table_x <- data.frame(garden_simulation_results$x[4:7,21:30]) #, ,32:41,43:70,73:76 also of possible interest
gtExtras::gt_plt_summary(mcSimulation_table_x)
| mcSimulation_table_x | ||||||
| 4 rows x 10 cols | ||||||
| Column | Plot Overview | Missing | Mean | Median | SD | |
|---|---|---|---|---|---|---|
| equipment_cost | 0.0% | 13.8 | 13.5 | 1.3 | ||
| construction_cost | 0.0% | 72.8 | 85.7 | 32.1 | ||
| garden_designing_costs | 0.0% | 11.7 | 11.9 | 1.3 | ||
| teacher_training_cost | 0.0% | 17.4 | 17.7 | 5.5 | ||
| school_board_planning | 0.0% | 7.1 | 7.2 | 1.1 | ||
| teaching_equipment | 0.0% | 9.6 | 9.5 | 1.9 | ||
| compost_starting | 0.0% | 8.3 | 8.4 | 1.9 | ||
| worm_starting | 0.0% | 6.5 | 5.7 | 2.2 | ||
| livestock_establishment_costs | 0.0% | 20.1 | 21.1 | 2.7 | ||
| fishpond_cost | 0.0% | 8.6 | 8.7 | 1.0 | ||
# a summary table with missing, mean, median and sd
The table shows the variable name, the plot overview as well as the number of missing values, the mean, median and the standard deviation of the distribution for variables that calculated in the model.
The full repository can be accessed at https://github.com/CWWhitney/urban_school_gardens